Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations. Issue 40 (5th September 2022)

• Record Type: Journal Article
• Authors: Valleti, Sai Mani Prudhvi; Ignatans, Reinis; Kalinin, Sergei V.; Tileli, Vasiliki
• Is Part Of: Small. Volume 18:Issue 40(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-05
• Subjects: ferroelectrics -- in‐situ heating -- multivariate statistical analysis -- phase transitions -- scanning transmission electron microscopy -- sliding window transform; Nanotechnology -- Periodicals; Nanoparticles -- Periodicals; Microtechnology -- Periodicals; 620.5
• DOI: 10.1002/smll.202104318
• Languages: English
• ISSNs: 1613-6810

Abstract

Abstract: Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions. Abstract : Temperature‐induced phase transition in BaTiO3 using an unsupervised machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy imaging data is explored. This approach is based on the multivariate statistical analysis of system descriptors derived from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform.